2011
DOI: 10.1073/pnas.1103723108
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Random phase detection in multidimensional NMR

Abstract: Despite advances in resolution accompanying the development of high-field superconducting magnets, biomolecular applications of NMR require multiple dimensions in order to resolve individual resonances, and the achievable resolution is typically limited by practical constraints on measuring time. In addition to the need for measuring long evolution times to obtain high resolution, the need to distinguish the sign of the frequency constrains the ability to shorten measuring times. Sign discrimination is typical… Show more

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Cited by 20 publications
(20 citation statements)
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“…the same but selecting either the cosine or sine-modulated component at random for each time-increment, as suggested by Maciejewski et al (2011) ( b ). For comparative purposes, a and b are recorded for the same total experiment time; a is recorded with and b with .…”
Section: Resultsmentioning
confidence: 99%
“…the same but selecting either the cosine or sine-modulated component at random for each time-increment, as suggested by Maciejewski et al (2011) ( b ). For comparative purposes, a and b are recorded for the same total experiment time; a is recorded with and b with .…”
Section: Resultsmentioning
confidence: 99%
“…The concepts underlying NUS can be embraced more broadly, enabling NMR to stray from the constraints imposed by the Nyquist Theorem and the Jeener paradigm, to sparsely sample phase [69, 70] as well as time dimensions. One can imagine that sparse sampling approaches could productively engage with “single shot” methods [71] that sample space instead of time.…”
Section: Looking Forwardmentioning
confidence: 99%
“…The majority of NUS schemes used to date collect all 2 d quadrature components for each sampled time point for quadrature detection conducted along d indirect dimensions. We recently described random phase detection (RPD, [26]), in which only a single quadrature component is randomly selected for detection from among the 2 d quadrature components, enabling a factor of 2 d reduction in the number of FIDs collected per time index, relative to conventional quadrature detection. RPD is one example of partial-component NUS , which as a class, includes any scheme that detects less than 2 d quadrature components for a sampled time point.…”
Section: Theorymentioning
confidence: 99%